In a different line of research, sampling methods rely on a point selection scheme that focuses on retaining the overall structure of the object, instead of its salient features. Among them, Farthest Point Sampling (FPS) [1]} remains the most popular choice and has been widely used as a building block in deep learning pipelines [2]}, [3]}.
Recently, S-Net [4]} was proposed as a learnable alternative for task-driven sampling, optimized for downstream tasks. To ensure that the selected points lie on the surface of the input point cloud, a matching step is performed using k-nearest neighbors. In a follow-up work, SampleNet [5]} extended S-Net by introducing a differentiable relaxation to the nearest neighbour matching step. Although the learnable sampling studies are closely related to ours, they only sample point clouds in a task-driven manner and as a result the preservation of the high frequency details of the point cloud is not ensured.
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